Buyer's Guide · Timeline
How Long Does an AI Consulting Project Take?
What actually happens week by week, and what slows projects down.
A typical DeployToday.AI engagement moves from first conversation to a live, working AI system in about two weeks, broken into five stages. Simple workflows move faster; projects involving multiple systems or a lot of internal sign-off take longer.
The five-stage timeline
| Stage | What happens | Typical duration |
|---|---|---|
| 01 Discovery | We understand the business, its systems, and where AI could help. | 1 day |
| 02 Opportunity Mapping | We identify the highest-value use cases. | 1 day |
| 03 Solution Design | We define the workflow, tools, and deployment plan. | 1 day |
| 04 Build & Rollout | We configure, build, test, and roll out to the relevant team. | 1 week |
| 05 Adoption & Improvement | Training, handover, and post-launch refinement. | Ongoing |
What actually slows AI projects down
- No single internal owner — decisions stall when three people need to agree on everything.
- Data or system access takes longer to sort out than expected.
- Scope creep — "can it also do X" added mid-build without adjusting the timeline.
- Waiting on stakeholder sign-off between stages.
- Switching tools or direction partway through.
How to keep your project on schedule
- Nominate one internal owner before kickoff.
- Sort out data and system access before the Build & Rollout stage, not during it.
- Agree the acceptance checklist upfront — what "done" looks like, in writing.
- Start with one workflow, not five. Prove it works, then expand.
Fast path vs. full build
- AI Opportunity Audit is the fastest path — it produces clarity and a roadmap, not a live system, so it's measured in days.
- AI Workflow Deployment is the build itself — measured in weeks, depending on how many systems it touches.
- AI Team Enablement has no fixed end point by design — it's ongoing support and training, not a one-off project.